def __init__(self, pstocks, dt_start, dt_end, n_ahead, capital, StampTaxRate, CommissionChargeRate):
self._strategy = None
# actual timescope of the back test, depending on the user setting and the available data
self.timescope = None
# number of extra daily data for computation (ahead of start datatime)
# set in the main program
self.n_ahead = n_ahead
# an output class instance for the report file
self.o = None
# check whether datetime is proper
assert dt_start < dt_end
# is_Day_cst: to check whether there is at least one daily candlestick data in pstocks
is_Day_cst_exist = False
# load candlestick(cst) data
# .stocks = {code(str): PStock,}
self.stocks = {}
for pstock in pstocks:
# get info from filename
# .ID(class StockID), .period(str)
ID, period = parse_cst_filename(pstock)
if period == "1Day":
is_Day_cst_exist = True
if ID.code not in self.stocks:
self.stocks[ID.code] = PStock(pstock, dt_start, dt_end, n_ahead)
else:
self.stocks[ID.code].load_cst(pstock, dt_start, dt_end, n_ahead)
# remind user
assert is_Day_cst_exist, "At least one item in pstocks should be daily candlestick data"
# for convenience, store sz cst information as class PStock
self.sz = PStock("000001.sz-1Day", dt_start, dt_end, n_ahead)
# for convenience, store dayily close price of sz
self.sz_daily_close = self.sz.cst["1Day"]["close"].values[n_ahead:]
# get sz daily time axis
self.szTimeAxis = self.sz.cst["1Day"].index[n_ahead:] # pd.DataFrame.index
# find missing daily candlestick data and fill them up in .stocks[code].missing_cst['1Day']
# as pd.DataFrame
self.fill_missing_cst()
# read candlestick data timescope from sz
self.timescope = str(self.szTimeAxis[0]), str(self.szTimeAxis[-1])
# initial capital to invest, a constant once being set
self._capital = capital
# cash in the account, changing during the back test
self._cash = capital
# stock shares in the account, changing during the back test
self._shares = {} # ._shares: {code(str): quantity(int)}
# records. add one record after each transaction(buy or sell)
# _records = [(datetime(str), cash, shares),]
self._records = []
self._records.append((self.timescope[0], self._cash, self._shares.copy()))
# set stamp tax rate and commission charge rate
self.StampTaxRate = StampTaxRate
self.CommissionChargeRate = CommissionChargeRate
# to store total commission charge in the back test
self._total_commission_charge = 0
# to store total stamp tax in the back test
self._total_stamp_tax = 0
# trading_stocks: To store the stocks that are traded. Note that although have been loaded,
# some stocks in self.stocks may not been traded in user's strategy.
self.trading_stocks = set()
def __init__(self, pstocks, dt_start, dt_end, n_ahead):
self._analysis = None
# actual timescope of the back test, depending on the user setting and the available data
self.timescope = None
# number of extra daily data for computation (ahead of start datatime)
# set in the main program
self.n_ahead = n_ahead
# check whether datetime is proper
assert (dt_start < dt_end)
# is_Day_cst: to check whether there is at least one daily candlestick data in pstocks
is_Day_cst_exist = False
# load candlestick(cst) data
# .stocks = {code(str): PStock,}
self.stocks = {}
for pstock in pstocks:
# get info from filename
# .ID(class StockID), .period(str)
ID, period = parse_cst_filename(pstock)
if period == '1Day':
is_Day_cst_exist = True
if ID.code not in self.stocks:
self.stocks[ID.code] = PStock(pstock, dt_start, dt_end,
n_ahead)
else:
self.stocks[ID.code].load_cst(pstock, dt_start, dt_end,
n_ahead)
# remind user
assert is_Day_cst_exist, "At least one item in pstocks should be daily candlestick data"
# for convenience, store sz cst information as class PStock
self.sz = PStock('000001.sz-1Day', dt_start, dt_end, n_ahead)
# for convenience, store dayily close price of sz
self.sz_daily_close = self.sz.cst['1Day']['close'].values[n_ahead:]
# get sz daily time axis
self.szTimeAxis = self.sz.cst['1Day'].index[
n_ahead:] # pd.DataFrame.index
# find missing daily candlestick data and fill them up in .stocks[code].missing_cst['1Day']
# as pd.DataFrame
self.fill_missing_cst()
# read candlestick data timescope from sz
self.timescope = str(self.szTimeAxis[0]), str(self.szTimeAxis[-1])
def load(self, pstocks):
"""
Download cst data and save them in .csv file (in /data/cst).
Args:
pstocks(str): filename of cst data
"""
end = datetime.now().date() - timedelta(days=1)
# download cst data for all required pstocks
for pstock in pstocks:
# get info from filename
# .ID(class StockID), .period(str)
ID, period = parse_cst_filename(pstock)
# get cst data file path
fname = pstock + ".csv"
pathname = os.path.join(self.dir_data, fname)
if os.path.exists(pathname): # if cst data file exists
# get modified date
file_mtime = time.ctime(os.path.getmtime(pathname))
file_mtime = datetime.strptime(file_mtime,
"%a %b %d %H:%M:%S %Y")
start = file_mtime.date()
# make the cst data file up-to-date
if start < end:
# download using tushare
df = self.download(ID.code, str(start), str(end), period)
if df is not None: # not empty
# update cst data file
df.to_csv(pathname, mode='a', header=None)
else: # if cst data file not exists, download data and create it.
# get listing date of the stock
ListingDate = self.stock_basics.ix[ID.code]['timeToMarket']
ListingDate = datetime.strptime(str(ListingDate),
'%Y%m%d').date()
# download using tushare
df = self.download(ID.code, str(ListingDate), str(end), period)
if df is not None: # not empty
# save
df.to_csv(pathname)
def load(self, pstocks):
"""
Download cst data and save them in .csv file (in /data/cst).
Args:
pstocks(str): filename of cst data
"""
end = datetime.now().date() - timedelta(days = 1)
# download cst data for all required pstocks
for pstock in pstocks:
# get info from filename
# .ID(class StockID), .period(str)
ID, period = parse_cst_filename(pstock)
# get cst data file path
fname = pstock + ".csv"
pathname = os.path.join(self.dir_data, fname)
if os.path.exists(pathname): # if cst data file exists
# get modified date
file_mtime = time.ctime(os.path.getmtime(pathname))
file_mtime = datetime.strptime(file_mtime, "%a %b %d %H:%M:%S %Y")
start = file_mtime.date()
# make the cst data file up-to-date
if start < end:
# download using tushare
df = self.download(ID.code, str(start), str(end), period)
if df is not None: # not empty
# update cst data file
df.to_csv(pathname, mode='a', header = None)
else: # if cst data file not exists, download data and create it.
# get listing date of the stock
ListingDate = self.stock_basics.ix[ID.code]['timeToMarket']
ListingDate = datetime.strptime(str(ListingDate), '%Y%m%d').date()
# download using tushare
df = self.download(ID.code, str(ListingDate), str(end), period)
if df is not None: # not empty
# save
df.to_csv(pathname)
def __init__(self, pstocks, dt_start, dt_end, n_ahead):
self._analysis = None
# actual timescope of the back test, depending on the user setting and the available data
self.timescope = None
# number of extra daily data for computation (ahead of start datatime)
# set in the main program
self.n_ahead = n_ahead
# check whether datetime is proper
assert(dt_start < dt_end)
# is_Day_cst: to check whether there is at least one daily candlestick data in pstocks
is_Day_cst_exist = False
# load candlestick(cst) data
# .stocks = {code(str): PStock,}
self.stocks = {}
for pstock in pstocks:
# get info from filename
# .ID(class StockID), .period(str)
ID, period = parse_cst_filename(pstock)
if period == '1Day':
is_Day_cst_exist = True
if ID.code not in self.stocks:
self.stocks[ID.code] = PStock(pstock, dt_start, dt_end, n_ahead)
else:
self.stocks[ID.code].load_cst(pstock, dt_start, dt_end, n_ahead)
# remind user
assert is_Day_cst_exist, "At least one item in pstocks should be daily candlestick data"
# for convenience, store sz cst information as class PStock
self.sz = PStock('000001.sz-1Day', dt_start, dt_end, n_ahead)
# for convenience, store dayily close price of sz
self.sz_daily_close = self.sz.cst['1Day']['close'].values[n_ahead:]
# get sz daily time axis
self.szTimeAxis = self.sz.cst['1Day'].index[n_ahead:] # pd.DataFrame.index
# find missing daily candlestick data and fill them up in .stocks[code].missing_cst['1Day']
# as pd.DataFrame
self.fill_missing_cst()
# read candlestick data timescope from sz
self.timescope = str(self.szTimeAxis[0]), str(self.szTimeAxis[-1])
def __init__(self, pstocks, dt_start, dt_end, n_ahead, capital,
StampTaxRate, CommissionChargeRate):
self._strategy = None
# actual timescope of the back test, depending on the user setting and the available data
self.timescope = None
# number of extra daily data for computation (ahead of start datatime)
# set in the main program
self.n_ahead = n_ahead
# an output class instance for the report file
self.o = None
# check whether datetime is proper
assert (dt_start < dt_end)
# is_Day_cst: to check whether there is at least one daily candlestick data in pstocks
is_Day_cst_exist = False
# load candlestick(cst) data
# .stocks = {code(str): PStock,}
self.stocks = {}
for pstock in pstocks:
# get info from filename
# .ID(class StockID), .period(str)
ID, period = parse_cst_filename(pstock)
if period == '1Day':
is_Day_cst_exist = True
if ID.code not in self.stocks:
self.stocks[ID.code] = PStock(pstock, dt_start, dt_end,
n_ahead)
else:
self.stocks[ID.code].load_cst(pstock, dt_start, dt_end,
n_ahead)
# remind user
assert is_Day_cst_exist, "At least one item in pstocks should be daily candlestick data"
# for convenience, store sz cst information as class PStock
self.sz = PStock('000001.sz-1Day', dt_start, dt_end, n_ahead)
# for convenience, store dayily close price of sz
self.sz_daily_close = self.sz.cst['1Day']['close'].values[n_ahead:]
# get sz daily time axis
self.szTimeAxis = self.sz.cst['1Day'].index[
n_ahead:] # pd.DataFrame.index
# find missing daily candlestick data and fill them up in .stocks[code].missing_cst['1Day']
# as pd.DataFrame
self.fill_missing_cst()
# read candlestick data timescope from sz
self.timescope = str(self.szTimeAxis[0]), str(self.szTimeAxis[-1])
# initial capital to invest, a constant once being set
self._capital = capital
# cash in the account, changing during the back test
self._cash = capital
# stock shares in the account, changing during the back test
self._shares = {} # ._shares: {code(str): quantity(int)}
# records. add one record after each transaction(buy or sell)
# _records = [(datetime(str), cash, shares),]
self._records = []
self._records.append(
(self.timescope[0], self._cash, self._shares.copy()))
# set stamp tax rate and commission charge rate
self.StampTaxRate = StampTaxRate
self.CommissionChargeRate = CommissionChargeRate
# to store total commission charge in the back test
self._total_commission_charge = 0
# to store total stamp tax in the back test
self._total_stamp_tax = 0
# trading_stocks: To store the stocks that are traded. Note that although have been loaded,
# some stocks in self.stocks may not been traded in user's strategy.
self.trading_stocks = set()
